Near-Memory Computing Architectures and Circuits for Ultra-Low Power Near-Sensor Processors
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Date
2023
Publication Type
Doctoral Thesis
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Abstract
Artificial intelligence has started to permeate the entire technological fabric of our interconnected world and has long found its way to the network edge. Be it as part of novel biomedical devices that constantly monitor patient health, smart sensors in industrial settings where they drive the transformation from reactive- to pro-active maintenance in industry 4.0 or integrated into the next evolution of human-machine interfaces like XR, ML-enacting near-sensor circuits are omnipresent. The tight power and latency constraints of these applications fuel a paradigm shift in the hardware architecture domain, away from conventional von-Neumann-based computing, which is bound by the memory bandwidth bottleneck of the data- and control path. The "new golden age of computer architecture", as the famous computer pioneer David Patterson calls it, is marked by innovative Near- and In-memory architectures around conventional CMOS as well as novel "beyond-CMOS" technologies like PCM or ReRAM. However, many of these techniques are explored with an isolated, device-level-focused view, whereas advances at the system-level demand a holistic multi-objective optimization approach that involves hardware-software co-design and the exploration of new computing paradigms.
This thesis investigates energy-efficient digital hardware architectures at both the circuit- and the system level and develops adequate strategies to enable energy-proportionality for general-purpose near-sensor analytics, i.e. the proportionality of energy consumption to vastly varying dynamic changes in workload compute intensity. We follow a multi-stage architectural approach where highly energy-efficient circuits based on the compute framework of Vector-Symbolic Architectures make up the first, always-on stage of our architecture "stack". In the second part of this thesis, we shift focus to the next, more computationally performant stage around heterogeneous compute cluster architectures, with an emphasis on the memory hierarchy. Here we propose a novel architectural design pattern to tightly couple NVM to the hardware accelerator. Both aspects are demonstrated and evaluated in several silicon realizations in 65nm bulk, 22nm FDSOI and 16nm FinFET technology.
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published
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Examiner: Benini, Luca
Examiner : Rovatti, Riccardo
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ETH Zurich
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Software
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Subject
near-memory computing; computer architecture; digital circuits; hyper dimensional computing; vector symbolic architectures (VSAs); hardware acceleration; Artificial Intelligence; memory hierarchy; Wake-up circuit
Organisational unit
03996 - Benini, Luca / Benini, Luca